{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e631810e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "12fa3ed1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# CMU Serif\n",
    "plt.rcParams['font.family'] = 'CMU Serif'\n",
    "plt.rcParams['mathtext.fontset'] = 'cm'\n",
    "plt.rcParams['font.size'] = 14\n",
    "plt.rcParams['axes.titlesize'] = 16\n",
    "plt.rcParams['axes.labelsize'] = 15\n",
    "plt.rcParams['xtick.labelsize'] = 13\n",
    "plt.rcParams['ytick.labelsize'] = 13\n",
    "plt.rcParams['legend.fontsize'] = 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c691c40e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Structure</th>\n",
       "      <th>Mode</th>\n",
       "      <th>Operation</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Связный список</td>\n",
       "      <td>Случайный</td>\n",
       "      <td>Вставка</td>\n",
       "      <td>0.199516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Связный список</td>\n",
       "      <td>Случайный</td>\n",
       "      <td>Поиск</td>\n",
       "      <td>0.024629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Связный список</td>\n",
       "      <td>Случайный</td>\n",
       "      <td>Удаление</td>\n",
       "      <td>0.014065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Связный список</td>\n",
       "      <td>Случайный</td>\n",
       "      <td>Вставка</td>\n",
       "      <td>0.196946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Связный список</td>\n",
       "      <td>Случайный</td>\n",
       "      <td>Поиск</td>\n",
       "      <td>0.023807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>Бинарное дерево поиска</td>\n",
       "      <td>Отсортированный</td>\n",
       "      <td>Поиск</td>\n",
       "      <td>0.062731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>Бинарное дерево поиска</td>\n",
       "      <td>Отсортированный</td>\n",
       "      <td>Удаление</td>\n",
       "      <td>0.062908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>Бинарное дерево поиска</td>\n",
       "      <td>Отсортированный</td>\n",
       "      <td>Вставка (среднее)</td>\n",
       "      <td>0.952690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>Бинарное дерево поиска</td>\n",
       "      <td>Отсортированный</td>\n",
       "      <td>Поиск (среднее)</td>\n",
       "      <td>0.060593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>Бинарное дерево поиска</td>\n",
       "      <td>Отсортированный</td>\n",
       "      <td>Удаление (среднее)</td>\n",
       "      <td>0.064886</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>378 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Structure             Mode           Operation      Time\n",
       "0            Связный список        Случайный             Вставка  0.199516\n",
       "1            Связный список        Случайный               Поиск  0.024629\n",
       "2            Связный список        Случайный            Удаление  0.014065\n",
       "3            Связный список        Случайный             Вставка  0.196946\n",
       "4            Связный список        Случайный               Поиск  0.023807\n",
       "..                      ...              ...                 ...       ...\n",
       "373  Бинарное дерево поиска  Отсортированный               Поиск  0.062731\n",
       "374  Бинарное дерево поиска  Отсортированный            Удаление  0.062908\n",
       "375  Бинарное дерево поиска  Отсортированный   Вставка (среднее)  0.952690\n",
       "376  Бинарное дерево поиска  Отсортированный     Поиск (среднее)  0.060593\n",
       "377  Бинарное дерево поиска  Отсортированный  Удаление (среднее)  0.064886\n",
       "\n",
       "[378 rows x 4 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_path = \"../../source/results/benchmarks.csv\"\n",
    "\n",
    "data = pd.read_csv(csv_path)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a3737f45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(0.023733), np.float64(0.193345), np.float64(0.014249))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Получение данных для Связного списка\n",
    "\n",
    "ll_random_insert  = data.loc[\n",
    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка'),\n",
    "        'Time'\n",
    "    ].tolist()\n",
    "ll_random_insert_average  = data.loc[\n",
    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка (среднее)'),\n",
    "        'Time'\n",
    "    ].iloc[0]\n",
    "\n",
    "ll_random_search  = data.loc[\n",
    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск'),\n",
    "        'Time'\n",
    "    ].tolist()\n",
    "ll_random_search_average  = data.loc[\n",
    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск (среднее)'),\n",
    "        'Time'\n",
    "    ].iloc[0]\n",
    "\n",
    "ll_random_delete  = data.loc[\n",
    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление'),\n",
    "        'Time'\n",
    "    ].tolist()\n",
    "ll_random_delete_average  = data.loc[\n",
    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление (среднее)'),\n",
    "        'Time'\n",
    "    ].iloc[0]\n",
    "ll_random_search_average, ll_random_insert_average, ll_random_delete_average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5434d260",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(0.034479), np.float64(0.193979), np.float64(0.024509))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Получение данных для Связного списка\n",
    "\n",
    "ll_sorted_insert  = data.loc[\n",
    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка'),\n",
    "        'Time'\n",
    "    ].tolist()\n",
    "ll_sorted_insert_average  = data.loc[\n",
    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка (среднее)'),\n",
    "        'Time'\n",
    "    ].iloc[0]\n",
    "\n",
    "ll_sorted_search  = data.loc[\n",
    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск'),\n",
    "        'Time'\n",
    "    ].tolist()\n",
    "ll_sorted_search_average  = data.loc[\n",
    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск (среднее)'),\n",
    "        'Time'\n",
    "    ].iloc[0]\n",
    "\n",
    "ll_sorted_delete  = data.loc[\n",
    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление'),\n",
    "        'Time'\n",
    "    ].tolist()\n",
    "ll_sorted_delete_average  = data.loc[\n",
    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление (среднее)'),\n",
    "        'Time'\n",
    "    ].iloc[0]\n",
    "ll_sorted_search_average, ll_sorted_insert_average, ll_sorted_delete_average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3deed9a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(0.0), np.float64(0.003635), np.float64(5e-05))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Получение данных для хеш таблицы\n",
    "ht_random_insert  = data.loc[\n",
    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка'),\n",
    "        'Time'\n",
    "    ].tolist()\n",
    "ht_random_insert_average  = data.loc[\n",
    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка (среднее)'),\n",
    "        'Time'\n",
    "    ].iloc[0]\n",
    "\n",
    "ht_random_search  = data.loc[\n",
    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск'),\n",
    "        'Time'\n",
    "    ].tolist()\n",
    "ht_random_search_average  = data.loc[\n",
    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск (среднее)'),\n",
    "        'Time'\n",
    "    ].iloc[0]\n",
    "\n",
    "ht_random_delete  = data.loc[\n",
    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление'),\n",
    "        'Time'\n",
    "    ].tolist()\n",
    "ht_random_delete_average  = data.loc[\n",
    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление (среднее)'),\n",
    "        'Time'\n",
    "    ].iloc[0]\n",
    "\n",
    "ht_random_delete_average, ht_random_insert_average, ht_random_search_average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "490e5c46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(0.000163), np.float64(0.003181), np.float64(0.000109))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Получение данных для хеш таблицы\n",
    "ht_sorted_insert  = data.loc[\n",
    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка'),\n",
    "        'Time'\n",
    "    ].tolist()\n",
    "ht_sorted_insert_average  = data.loc[\n",
    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка (среднее)'),\n",
    "        'Time'\n",
    "    ].iloc[0]\n",
    "\n",
    "ht_sorted_search  = data.loc[\n",
    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск'),\n",
    "        'Time'\n",
    "    ].tolist()\n",
    "ht_sorted_search_average  = data.loc[\n",
    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск (среднее)'),\n",
    "        'Time'\n",
    "    ].iloc[0]\n",
    "\n",
    "ht_sorted_delete  = data.loc[\n",
    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление'),\n",
    "        'Time'\n",
    "    ].tolist()\n",
    "ht_sorted_delete_average  = data.loc[\n",
    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление (среднее)'),\n",
    "        'Time'\n",
    "    ].iloc[0]\n",
    "\n",
    "ht_sorted_delete_average, ht_sorted_insert_average, ht_sorted_search_average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9d7274ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(0.000481), np.float64(0.006081), np.float64(0.000336))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Получение данных для дерева\n",
    "bst_random_insert  = data.loc[\n",
    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка'),\n",
    "        'Time'\n",
    "    ].tolist()\n",
    "bst_random_insert_average  = data.loc[\n",
    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка (среднее)'),\n",
    "        'Time'\n",
    "    ].iloc[0]\n",
    "\n",
    "bst_random_search  = data.loc[\n",
    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск'),\n",
    "        'Time'\n",
    "    ].tolist()\n",
    "bst_random_search_average  = data.loc[\n",
    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск (среднее)'),\n",
    "        'Time'\n",
    "    ].iloc[0]\n",
    "\n",
    "bst_random_delete  = data.loc[\n",
    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление'),\n",
    "        'Time'\n",
    "    ].tolist()\n",
    "bst_random_delete_average  = data.loc[\n",
    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление (среднее)'),\n",
    "        'Time'\n",
    "    ].iloc[0]\n",
    "\n",
    "bst_random_delete_average, bst_random_insert_average, bst_random_search_average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "92a545c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(0.064886), np.float64(0.95269), np.float64(0.060593))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Получение данных для дерева\n",
    "bst_sorted_insert  = data.loc[\n",
    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка'),\n",
    "        'Time'\n",
    "    ].tolist()\n",
    "bst_sorted_insert_average  = data.loc[\n",
    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка (среднее)'),\n",
    "        'Time'\n",
    "    ].iloc[0]\n",
    "\n",
    "bst_sorted_search  = data.loc[\n",
    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск'),\n",
    "        'Time'\n",
    "    ].tolist()\n",
    "bst_sorted_search_average  = data.loc[\n",
    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск (среднее)'),\n",
    "        'Time'\n",
    "    ].iloc[0]\n",
    "\n",
    "bst_sorted_delete  = data.loc[\n",
    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление'),\n",
    "        'Time'\n",
    "    ].tolist()\n",
    "bst_sorted_delete_average  = data.loc[\n",
    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление (среднее)'),\n",
    "        'Time'\n",
    "    ].iloc[0]\n",
    "\n",
    "bst_sorted_delete_average, bst_sorted_insert_average, bst_sorted_search_average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b2e93d6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# countUsers           = 10_000\n",
    "# countRepeat          = 10\n",
    "# countRandomSearch    = 200\n",
    "# countNotExitstSearch = 100\n",
    "# countDeletes         = 500\n",
    "\n",
    "countUsers           = 20_000\n",
    "countRepeat          = 20\n",
    "countRandomSearch    = 1000\n",
    "countNotExitstSearch = 500\n",
    "countDeletes         = 1000\n",
    "\n",
    "ll_col = 'blue'\n",
    "ht_col = 'orange'\n",
    "bst_col = 'green'\n",
    "\n",
    "iterations = range(countRepeat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "208784a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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g9t/H4MBAvixfntZJfZYrMzU4Yntxtndxytb66O4CZQB7yqr0d2cE7zAo6rtb0Pe2KNo/g+9RnUdvD+2Dc+fODT9X1q2+I9F3QMTzG1mS37+i1itWuRogS9+xESNGuLtO9BuhutVvZLyUia8M/XhpP1cWte4OKOq7EYsGo1LWeLwP7WclobsSdOdG9F0Ase4m8NOCr23vfNHTC3oe6/3KTvZ3jAAAMhOBWADIUDpxlSVLlhQ579KlS/NNizWAl26X/eGHH2z27NnuuQKAuiVbt63qlkWddChYp4HCCqPX1WWBbhHea6+9irFW5m5F1fLqJEzdIviHTsJ1i6UPFm4PrcuNN94YMU1BT3WloMBxsFzd1qzbrnVrZqIVtQ1efvlldwIfDMB4mhYdEA/yt6UH6fZF3WYapACfbm0PBsxi0e2ver9uSxedoOtEXSejQTqZV5Ao1rLFG7jydBun6l+3DAepq4Lo/vmCdKuouojQexX8UwA/uE0VjPXBwHRSHao7BN1uHaSAlIJLvg41uIuCacH6Ux189NFHLiAbb33EoluQVb/Ry1Bc0fuHjhkKAgS7TlGATbdG6+KGqGuDwqhrB9Ht0DrOLVu2zAV3tcw6LhV1HPJdk2hAw7/97W/5XldgV8cUXZhQ8Kekx47iUvBXF0S0XkWVq+OebhWP7oJG328Fq4pTj4UddxTYVkAx2PWMgmI6Pvj+JLXc6uuyoBHsdeFNXZ9of1OgOVpxlrM4ZRe0PhLsliJZdFFMAcrCKOCqALW6Lxg8eLAbiEmK2ofTTfudgurqjkPdPagrieLWTTzdEgSpnaHjmS6gxer+Jd3GjRvnuqt57733Ira7LmZFd8sj/mJC1apV3TyyvfNpHl9mrPmCnxVN24PBugAgsxXvkjAAIGXU96FOmtVnnE5+C6OgqPo4LEq9evXcX/UnqACRPltZTcrEU1+CCqQUFvxTH3jKxPzkk09cME4ZSdHBNwUVNF8wABrkA8vKGFJQMkiZa7ECQMGMG581V1hdKGsmemRjX64ClT6Y5BU24nOiBbeBspElVoBUAcZghlWQTvKVLRcdJA1SH4gKPP/nP/9xmYCxMoN0oqm+SdW/obKhFPhr1aqVe03LVlDgtqBlixUwKYyCw9pv1f9jUPPmzQt9n4KTfptqWRK9/fS90D6swJD64NPFBgUNooOZ6p8xmNWlfd9fQPF16Je1sDpU/5EKthYk3vqIpkzjYB+HxVXY/qFlloKCdvEst39d21FZ4AqoKoCp766m6YJFLKp39ZH8448/uos6Wq5g5rmnbG5dZNEFCx3ngpmjxTl2xEvHKtWH9h/tKwpK66JAYRJZj/EedzwdP3QMVmBMF9UUfFLgOhZdIFOQXK/feuutrl6VaVvS5SxO2fGujygLVd9dXVjT90/LqDsQCrsIVRT95hS2fXwGpTJ8tW9q8ExlJAYz3GMto/fll18W+h0M9ner9xa0T/nfSgW4ldmtfb5Dhw5Frp8y9HUxS98ltQGKq7jHfV0k0wUPfS8VuM4kOtapb2XdyaB+dYN0x1CsPln9XTzapxW4VfDUT4ueT4FTzaPPCr43er7GjRuHy/TTgkHhYJnRCusDHACQGQjEAkCGUsaQTlYUGC0qQyv6VvSC+MFDdIKhIJOCH7qNO/p2UE9ZmwrYerrVT3Rb6NixY91t9gqiKYDi6cTAzye6/To4sIo/uQkOZOJp5OLowK4CiMHgVvAENlZWkk7Adat29K15hZUryswpyUlocQW3gb9VVydV0cEfBT7860E6EdQgKgqKFEYn4XrcdtttLtChIIECuMEMGmWVaeAyUcBNJ+0KUqvrCJX9zTffxPzsWMumk//iBDt8AKaoTLPCqA51wqzsoOjvQPT2VDZgYVml0Vlifh9WlqYGQdIATxr8Krh/avk1UJIXHVBWHcU60Y6uQ/0tasC44vIDoRX2fSlKUfuHaLn9CN4lpUGuVB/Dhw+PeSyLPg4F613BNmWeKntUA7AF6UKRAmjKOj3vvPPc8SFWBllRx454BY9VChQrIOcHoytIsB5TcdzR4FdB6hZBt6LPmTPHDbykQaRi0XZRdxra/xX4Vpa1ju3bc8yMt+zirI/uJPDfXX331KWBughRcLykYmUtBikrVwOR6W4PbfNYmcTKUvQDLgWXUXTHwqhRo4r8Doou/BS0fwb3P2V16rilQRyD749FxzYtj34rFJBV2yNe+t3SsaG4NOik7gRQvRWnKwTdZaALXvHSb5IyleOh76CCwzqOBwP92td0MeuAAw6IOdimLhrpt8XfHaT59LsRa77999/f/V/dh+hiTUHzqW3mP0s0X/C30l9Y9p8XpCB8SS8mAQBSg64JACBDKatCJx3KatTJVEHUN6JvrAcpczWaMrQUZFKWkDLcdJKhzKZYGVqiIEJBFMBVBo1uaywO9SOoYK2yaqPptnKdRJSEAm06idRo07HoBFUZcbHK1e2xqo9EK2ob+MCRMvui6YRb/VpG04jkykiLRSdruqU7SCeICngoYFnYSMoKFOjWYdWfgng6Oda2CO4P/lbb6dOn51s2ncxrn4iHsroUJInVp2RxKNCsgOtnn32W7zWNUC4+g0mjvceTORhNt/wrwKH6K+4+ojqcNm1avm4v1D2G6tjXoQKLyooL9pfpl93f4lxcWn99N4ubrVac/UP8qPdBwQsv8dB+qYBTMAgb73FImWO6TT46y118FqMyXbUNlBlekmNHSShAouzEWFn+QQrSKps3Vj3quB/rGFLS445+U3QnQpC+swoGKzu9sAx7ZWH6/kn1u/TTTz+5vo23R7xlF7Q+WhZ9dwqioLsuGvhs7pLQ97eobk60/ypIpgsCsfZf9WMdnbmbijtqdKH04YcfLnQ+HYvUxtC+r++t/kbfxVIY/Y6VJBCrbaf9WxdfY+37BdEFAGUax/uINwir3xFd8FYd+CCs6Ljsj986Xiso6/tZ9hSc1b7sA+36XYp1EVPz+X6VNa/6kI+eT5+vIKvfr7VPaXmi59Nn6U6IWHdLaXsEL14DADIPgVgAyGAKfCg7QxmoyhaKPiHVbee6/dj3lxekAE7wNn71R6rMspEjR4ZPoHSiqqCap5NVDY6iIJne6/udjNWPm07E1eAPZmTopCr6tjhNC/5VYFC3UGqgJT9Ikz8B0UAwvhsCP3+sz/OvBacpOKQApQ88RZcrOvHT7c6+X0LR+7QcsTKZosUq2wfPoqfHsw1Ud+oSQpnPwTpWBpcyeXSLZHQ9KuDkT/ii11G3uyvoFL0syuRU9o3P2Im1Pf0JsbqcUMBGmWTnnnuuu+U2SJmHOjkMnuAqYKhgR7C/YL/dosvy+4gyoqLXLVYd+jqOlZWmoJKyt3RrrYJpngJFJ554ogu+KlCn74lufQ5mr/rlii4zVt3oe6HsWx/IjXffVBDowAMPDPf16d+jANYFF1zgTsRF21mBzugsZw04FiubsqD6CC6btl2w39hY+25B4tk/lCGmwIW6lggGYhSUipUh678nsZZbGYLB45Au8ChwpaC/6ssHaGMtl+bR9zf6OCQ+gKIgmb4XCrY+99xzJTp2FCbWcilYo9ucY2WsBem7rMGS9J0PdsWgLEp9r6K7wyisHoPH5mCXGVoOlaGM4+i+hBVcVf+gysj0fftGUz0ELyYoyKPfIwVQCwpwxrOc8ZTtLzAp8BrcP9SHri42BPvXjrUddAFF2YEK1kfvF0XR760ufEV366J9MliWlkG/XdpmnrKF9T3Qb6n2L/+diOc3sri/f7HWW/uOtrsP1EVvQ1HAT8chf2FP/9cxUtnj8daRgr06zhZEQV1lPMei3wv9/mUCdWOi/UpZ3+onVw9lCev41rJly3AWqy5qqg/r4Pdc2cf6DffUjY32be17ngZD0zRl/3v6HVD3U8FxAPTZ+k3z7REd+/T7Efxd119dtP7nP/8Zc4A2XeiMpz0DAEgfuiYAgAynjFWd6GhQGgVcFRBSppduCVXWS0FZXgpSKbtD8+kEQycMClj47B4FXBWgUqBNJ5E6WVXmhQIV6qtPwSFl1CmjRrdOik7YNJ+CJMrE1YmEgsQKNqpvR/VnpxNPvVf98mmEdQU/RNkp3bt3d+ujALMCNjqR1km91kcZVz57TCfm/n0KsulkXQE1LYcyjRTQVABDJ3EKCOi5Tvx1sqKsFc3js4h1wq4TZJ1c6uREt8Bq+XRipKCEgi9Fdf0QvX46OVNAVOvnR1UOrl+820BUB6pznbxpHRXU0vwKevlb/X0QSRSwUSat5vEBXZ2UqasJBf4U0NDtlQrg6IRNGU+qG50U6vZufa7/LNWblknrpO4OtFw6ufMBKX9ru04etW/ohFEZbDp51Ami1kkD06iutc+orj0FAfz6KcCk7aJgkG7ZVlBCfQOqb1ctg6aLgrsKBvjgo/Y9Ba61bMr01f6nE3/14+mpDjSfAo86sdc66/0KgKpOFJBVEFZ96mqfj96//HbThQlN0wUBfS+0f+n9vk/GCRMmuEC31t1/HxSkUZ2oPAW5lKWkgKICqvpeKgCok3R9T6666ip3kUEBEi1LMBCtIKyyerWvq57U1YcCMFqnYP/IRdVHcL10AUfbSN8v1VH096agTNni7B9afwWnNVCW1kkXdtSnofZlTxnoCkrps0TfZS2zukPxGavqV1P1oTrSPqrAktZF3yXNr/1a29Avl+pat5qrOwP9VV+j/rb24HdFZSiooQsHPtCrQIr2QQVOFHSJ99hREAX9fXmqG32H1U2Hjo/av6MvZMSibDptLx2HFOjWfqoASzBI5Qf3U12KjmE6lukWZgWIgrTva3vpeKVAtTIedfGpoP5CVce63Tu4r3n6/qpc1YOCU7rNXd8z1ae2U+fOnd2+7rNj49ne8ZbtqfsB/UZpe6hMbT8dj/0dBeqeQsdATdf+qvkUSNSxT8dTLY8yW7Xf+HXS+qjsgij4pe5cdAxWWZ4u+Oi9Wj9tI62XtrO2uY6TCmTquKF9Seur7a/vg5ZD+4e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S++nLL7/sll+Zuv5uM931GPx+AYWhj1ggy+nHR43saLq1WLeUq2Hz9NNPu1vJfENVDb1gQ1HUKNIPjg9O6fahogTnUaBMP/bB4JYaf/L7778X+VlqHN97773u9ibdxlWQJ5980gWMdCIRHfSUatWqueBSdJBNy6KG3bJly9w8RYm3noLUmIvVgIhFJ0H6cX/00UfdOok+V40c3eIUi7+NvChHHnmkrV69OmYd+OBqYXTLmxq+5513nrtFJxPotiptczWmFUDUdvzHP/5R4iBsvBK1P2UafV91sqETOt1CqFvytG8DAEo/2obJ+S1XkEsBPd3KHE0BqwMPPNCtr4KeChbWqFGj0M9T/WqZFBRUAFZdBtWsWTNinu+//z78f3XBoGCvgpWxKACtulCdKdAcbEPFarfq4rYCS6o3BUaLu31j0a3pCpKqrgoS6yKxllFtkgcffNB1q6CgsILkumU/uk78uioIq/1Y3TEVRF0YvPrqq27/0Wd5b775Zr5+Pouzb2YitdsVRNZ2VbcL6s5gewKH6jYhnu+F6l/dMQTrUttT3SWU1rpUYPuRRx5xdalzj7feeivcrQsQDwKxQI7Sj58aJ+rnplOnTq5/UvVVpSxMUf8+0YI/oBdeeGGRZUTPo8as+nxSn1y6mljUj7caxQqmqQGqDEw1WvXeqlWr5ptXn62rq/r8OXPmuPc2bdo05udqPdT4+Ne//uX6PVX/p9GZp0WJt56ClAXgs1eLoj63FIzVNlLmgPpFeuaZZ6xnz54x51fWhIKo27ZtC1+RVsBU2a96b6xlVJ9L6mNJQVnVgTJQiqJGqU4g1PdSrKvY0X26FaeB5be31kHvU+Ba/d+qD6p4qAGtk7pLLrnEnWTE2jbFEW8n+yXZn1566SWXleNpOxXVb63qJrq+CguO+vl1IrJ8+XJr3769a4THczHAX+1XlrROFNWABgBkN9qG29c29H3O7rzzzjFfr1Onjgtcz5gxo8D2XJD6cJVvvvnG3cVUVLsmnvLVxtLnqW/6oigDWnxfsSXZvkEKvipT2Ac0iyu4PApqq916zDHHFLiu8uWXXxbYPtX2VmBS/cCqj9RoqnO1kxXsVlsoevyHaBp/Ibqt5rOJY9Fr0fPrM+JpPyoIqrEi1C+vMrcLStKIpgQPBev1nu3t7199qyoLOl66oKPvrNqhqs9gBnpB3/PotnBhlPEbPOeJ7oO2oO2lTF31U6zvvL6XCrLGQ/N+8MEH7gKBvnvxJtsAwt4C5DANuKRGjRqE6kRdASx/u48aaomkRoM60tdVwwceeMB23XVXN72wDAYFmfwtH6IfO93ypJMEDRYQpEwNPwCW1kvZAMoMiJURqYwOXVHXlXQNYqB5FOQsTsfqJaknBcRiBUULooGTNAiW1lcd1qvhGWvwDN+41ZVZXXVWA1zLpb/Rt/D5W4S0rdU4feqpp8J1qROtohpluv1LneyrsVIUDSYRfaKhW+vi3d66sqwGtgYlKOx90cFYdb6v7avlLOmt9MoujpU1E0tJ9ic12vyAFqIMGp0IFET7fbBuRNk9hQnOr5MInVQrq0VX7WPtF9HUuFZmy7vvvmsffvihez8AILvRNix529Dfxl5QPemCfHC+eMVb/4kuXxffJRGjvysopu61FPiKNehWcZdne9Y12D7V+xWUVDv6ueeeC2cpq62kgaPUXZPutNIFCS13cBDdWHecRbfVNABcQZTJGz2/gr3K+I2n/aiECw3wq/a/2vTxtHl1sUMX2fXdVlJFrMz4eCmhI9iWLcj06dPdcitYrPrwgXh91+L9nosGGQsOXhdNmda6MONpXmX+xrO91O7XcUcXCtS+jme9RO1ktZF1nqKgfjzta0DYU4Ac5xujyooM3lJUUCOpsCu7BVFDQSNI6kq++vzyDW3foPKKyshUUE4/3mpARS+HsjfUAFeAUKOEKqgYK3inW7vUoFKDX1ft/foHl0UB02CfUrGUpJ6UYRHsO60outVODQFd4VU2x7HHHlvgvMoEUdaHAqXKWNUoomqQxLpdS7eXvf76666hETxpCdZBrG2hK+hqsEb3rZYsCqTqNjxtr6K2h9/PFLhW0FG3tSmjOHofi5eubKuhWpRE7U/JphNQNTA1Iq3vG6wwWn4F/zUabOfOnV2mSPQo1gCA7ETbsGS/5b5/1ILuBlIGptonPrMzXq1bt3bLVFD9K+ioRzzlB5czngCar8ftoYxDBbq3J+gXvTzKeFXgcXvXVYEz9d+rTOhevXqFuys444wzXLtbwUIFYUuybyab6kDBVGWP6m88dMeYLn7o4r8uUihDtaR0t1VRCSbaL/U9VxLI2LFjI7KhizrvSCV9v9RtiJapoKSXaKo7ZearXa27EpU4AsSLQCyQw3Qlc9q0aS7zz9+ipAat+gRT30ixFJalUNgPlRrzGsAgeFVdgckgNZKLogasbsfRj39BdAu2GnvKbNAtRUFqBIg68A8KLosyT4u6/SXeelJ5ugXMd+QeT/9aQWoUqnGiwKpu008ELZMCc+pUvqA6iLUtlBlSWD9byaDMBAV/1T9bYTSPAodqWOqKu26r15VwNThLYsyYMRGDZBQkUftTKvgsD39yUhg16NWNgbJiR44c6fq2U2C8pIFtAEDpQNuw5L/lyp5U0DdW1p4CUcpa1GCYsfpALer3WxeXlUG8ePHimP2uaroChwraxirfD2imQdmCWYO+e6TozFI9VzahEgii66W41JWVkgXUL2g8Ym1HbV/tCxqITMF17TPa75QlGSsYq6xSZX8q27Uofnv47a2L1sqkLWx/iHffzKS23SeffOICh9pP1DesLl4oU7Ww701BdIzQHWhFUfcmuoBw2mmnRXTPpgzU4F14mVCX2j+1jPHUpeZRoowu4KibDGV66/iijHsgHgRigSylBpf63inolh3dcqZbtPS6GqW+g3r1b6NO69VXjm7Nis4YKKgx5vv58SPPRmd26kpjsE9MLZ8CPApMqgGg266Ct4rFGnjql19+cZmhCpAFr6hqXjXQgpTxoECjGq5a1+j+toLLogacHx1ey6KgX1G398RbT2rgqKGoZdTyR/elpOmFDbKlQZL0Hl1NDo5MWpwshOjPVx0ouBtseKlxrqxanRRp/uCtNf79Ws/gyZKfHv35/nlwdNTg81jrq2nR+6oyW3Wbj2670gmgf1/0ttbn6tZJZZj4+tUoyqp3BbCL6m8rmrpEUJ9b0ScMfvmC61Xc/am4dVPQ/H5aQXUZXF5RlpD2S93qpZNeP190XYpOvHSrp/Y5UbbDQw895E5MCus+AQCQ2WgbJrdtqMxJ3fKtboCCbQ/VZ+/evV0XDAVdIC6srkR3PKnOdEt9sG9NXexXHen3XdtJt9gr81fbK0iDq6r/XdVvNNVV9O+7AksK7ir4Hqv/Xb/MBS2v/1xRffuB34LTC2r/zps3L2LgI+0XSgTQ8mv9fBtVt5UrsKw7wILtJAXNhw8f7rqVCAYLCypPdeu7BBMlK0TvD3qv+pZVP8rxtu2KaqsVNH+sZY3VttP/lcWt4KHP5i2oraxkEH23ddecp31Bmem60F7crtbURcPll19eZDtZfTLre6E7zYIX89WuVFvdn4tEn3eUpN1b3LZ19DorsLpmzZrwuAgF1aW+F7pIpa4rfDdmSgbRfqOLMdxBhnjQRyyQZdRoVABKjRj/Q6AsgGAn7urcXR2sq0GoTtt1+3eQ+sdRw06NbV01VeNJjTvdJq8GT/AHTrfz6AfJ3+6sEdZ1tVrBHt+Q0dV5NZI1rxpjaijrvbo1Rp/pG5W6kqhbg5SNqOXTD58aDAr+qfGvwNwNN9zgrj6KGrlq7KpxoR93NaDUyNCVco34riv8aiyoDyBlIOi2KDUcdOX7lltucbfYa1AsNWDUZ5EamloW/Yj6YFVh4qknvabl1BV5Lbun9VSdaT2VmaEGphpC0Rmz+jwFxPRjXxzKBFBjVXWj+rv66qvtrLPOcvuC6lf1qnU8/PDDXSNCQUzVpRoWvnGhq9jqc00NT1G9a5RarZ8afhowQFTvGmVZjRBtQwXsRPNocAyVpTJ1m7uoca1MAzUaNRCG397KFtFJkhpj2nfVkNaJgObTbYvqTkHUh5fer/KUpTtq1CiXEaEMD53oKHCo/UXLrfm0bNrXihrxV1fjdQKlbRPcz4KDiYgykw899FC3XvHuTzpB1Dy+DvReXTXXiZEGGFOZohNf1ZlOLnTy479XWhetr/YjDfShdY7etlpvjYasfs1E9abMHM2vLB7Vg+pXdaWTE71fjVDViy4Y6Oq+unZQxoSySPQd0v4hOqERvV997ul7pi4LAACZj7Zh6tqGHTt2dOXozhK1ofR7quVWd0cK9ERfVNfvq7KQ1WYT9Wer32G1M3QxPpj5qCxitYvUptCAQlpOXYDWRWdPwVptJ912rval3qdtofpVYNFnUAbVrVvXbW/Vo5ZPGYxab7XRorMe1T2A2l66fV/7kgJpqkcthwJY/jb+YHtQQWFtN7Uh1Ab0QeJgezA4+Kj2EbVp+vbt6/Yxlak2ntYruM/qNe0X2k5qx2gZtM+oLO1bPsAu2j983/oKQCohQbfNK5CvJATtq34/1iCx2h4K0CprU1nB2jd9sE37ifq81TZWQNG3u/z6KNtUdad2q94fbOerLL9/KtCnrjD0/dCyqj2o9q5of1MigvY5ZTz7dqK6SlCfpFpHXUzQfqsgp4LCKkPtQ9E+r+4V9L3TcqqetGxq5/lb77V/ar3UxtR3TvtbYYOzaj/S/qe2sroU0T4WpP3Yf662g7aJ9ntlaKv+tS7aP7UcWjedU/jur7TvKTNdbV99X7Rvab9QPWif0jZTPStQqrawMmy1bsq29tm0Cshrus4FdJwKnr+o6wDVo9rZfntp+6gdrDpQmTo+qp2uelD73GfK63ij44r2X9Wx5tF+r64MfF+yOu/S9tD21XdX5woqN9b3DZAyIe4zBICMpcaBGnSFdfafyxQgVeA2EQNJqDGlBqMa2mr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",
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Создание двух графиков рядом\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n",
    "\n",
    "# ============= Левый график: случайные данные =============\n",
    "ax1.set_title(\"Вставка случайных данных\")\n",
    "ax1.set_ylabel('Время, с')\n",
    "ax1.set_xlabel('Повторения')\n",
    "ax1.set_xticks(iterations)\n",
    "\n",
    "# Связный список\n",
    "ax1.scatter(iterations, ll_random_insert, label='связный список', color=ll_col)\n",
    "ax1.axhline(y=ll_random_insert_average, color=ll_col, linewidth=1, \n",
    "            linestyle='--', alpha=0.7)\n",
    "\n",
    "# Хеш таблица\n",
    "\n",
    "ax1.scatter(iterations, ht_random_insert, label='хеш таблица', color=ht_col)\n",
    "ax1.axhline(y=ht_random_insert_average, color=ht_col, linewidth=1, \n",
    "            linestyle='--', alpha=0.7)\n",
    "\n",
    "# Дерево\n",
    "\n",
    "ax1.scatter(iterations, bst_random_insert, label='дерево', color=bst_col)\n",
    "ax1.axhline(y=bst_random_insert_average, color=bst_col, linewidth=1, \n",
    "            linestyle='--', alpha=0.7)\n",
    "\n",
    "ax1.legend(loc='best')\n",
    "ax1.grid(True, alpha=0.3)\n",
    "\n",
    "# ============= Правый график: отсортированные данные =============\n",
    "ax2.set_title(\"Вставка отсортированных данных\")\n",
    "ax2.set_ylabel('Время, с')\n",
    "ax2.set_xlabel('Повторения')\n",
    "ax2.set_xticks(iterations)\n",
    "# ax2.set_xticklabels(range(1, 6))\n",
    "\n",
    "# Связный список\n",
    "ax2.scatter(iterations, ll_sorted_insert, label='связный список', color=ll_col)\n",
    "ax2.axhline(y=ll_sorted_insert_average, color=ll_col, linewidth=1, \n",
    "            linestyle='--', alpha=0.5)\n",
    "\n",
    "# Хеш таблица\n",
    "ax2.scatter(iterations, ht_sorted_insert, label='хеш таблица', color=ht_col)\n",
    "ax2.axhline(y=ht_sorted_insert_average, color=ht_col, linewidth=1, \n",
    "            linestyle='--', alpha=0.5)\n",
    "\n",
    "# Дерево\n",
    "ax2.scatter(iterations, bst_sorted_insert, label='дерево', color=bst_col)\n",
    "ax2.axhline(y=bst_sorted_insert_average, color=bst_col, linewidth=1, \n",
    "            linestyle='--', alpha=0.5)\n",
    "\n",
    "ax2.legend(loc='best')\n",
    "ax2.grid(True, alpha=0.3)\n",
    "\n",
    "# Общая настройка\n",
    "plt.suptitle(f'Сравнение производительности вставки в структуры данных (N = {countUsers})', \n",
    "             fontsize=14)\n",
    "plt.tight_layout()\n",
    "plt.savefig('../img/insert.pdf',\n",
    "            format='pdf',\n",
    "            dpi=300,\n",
    "            bbox_inches='tight',  \n",
    "            pad_inches=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7de42c9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Создание двух графиков рядом\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n",
    "\n",
    "# ============= Левый график: случайные данные =============\n",
    "ax1.set_title(\"Поиск в случайных данных\")\n",
    "ax1.set_ylabel('Время, с')\n",
    "ax1.set_xlabel('Повторения')\n",
    "ax1.set_xticks(iterations)\n",
    "# ax1.set_xticklabels(range(1, 6))\n",
    "\n",
    "ax1.scatter(iterations, ll_random_search, label='связный список', color=ll_col)\n",
    "ax1.axhline(y=ll_random_search_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n",
    "\n",
    "ax1.scatter(iterations, ht_random_search, label='хеш таблица', color=ht_col)\n",
    "ax1.axhline(y=ht_random_search_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n",
    "\n",
    "ax1.scatter(iterations, bst_random_search, label='дерево', color=bst_col)\n",
    "ax1.axhline(y=bst_random_search_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n",
    "\n",
    "ax1.legend()\n",
    "ax1.grid(True, alpha=0.3)\n",
    "\n",
    "# ============= Правый график: отсортированные данные =============\n",
    "ax2.set_title(\"Поиск в отсортированных данных\")\n",
    "ax2.set_ylabel('Время, с')\n",
    "ax2.set_xlabel('Повторения')\n",
    "ax2.set_xticks(iterations)\n",
    "# ax2.set_xticklabels(range(1, 6))\n",
    "\n",
    "ax2.scatter(iterations, ll_sorted_search, label='связный список', color=ll_col)\n",
    "ax2.axhline(y=ll_sorted_search_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n",
    "\n",
    "ax2.scatter(iterations, ht_sorted_search, label='хеш таблица', color=ht_col)\n",
    "ax2.axhline(y=ht_sorted_search_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n",
    "\n",
    "ax2.scatter(iterations, bst_sorted_search, label='дерево', color=bst_col)\n",
    "ax2.axhline(y=bst_sorted_search_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n",
    "\n",
    "ax2.legend()\n",
    "ax2.grid(True, alpha=0.3)\n",
    "\n",
    "# Общий заголовок\n",
    "plt.suptitle(f'Сравнение времени поиска в структурах данных (N = {countRandomSearch} + {countNotExitstSearch})', fontsize=14)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('../img/search.pdf', \n",
    "            format='pdf',\n",
    "            dpi=300,\n",
    "            bbox_inches='tight',  # обрезает лишние поля\n",
    "            pad_inches=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0fd42f30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Создание двух графиков рядом\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n",
    "\n",
    "# ============= Левый график: случайные данные =============\n",
    "ax1.set_title(\"Удаление в случайных данных\")\n",
    "ax1.set_ylabel('Время, с')\n",
    "ax1.set_xlabel('Повторения')\n",
    "ax1.set_xticks(iterations)\n",
    "# ax1.set_xticklabels(range(1, 6))\n",
    "\n",
    "ax1.scatter(iterations, ll_random_delete, label='связный список', color=ll_col)\n",
    "ax1.axhline(y=ll_random_delete_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n",
    "\n",
    "ax1.scatter(iterations, ht_random_delete, label='хеш таблица', color=ht_col)\n",
    "ax1.axhline(y=ht_random_delete_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n",
    "\n",
    "ax1.scatter(iterations, bst_random_delete, label='дерево', color=bst_col)\n",
    "ax1.axhline(y=bst_random_delete_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n",
    "\n",
    "ax1.legend()\n",
    "ax1.grid(True, alpha=0.3)\n",
    "\n",
    "# ============= Правый график: отсортированные данные =============\n",
    "ax2.set_title(\"Удаление в отсортированных данных\")\n",
    "ax2.set_ylabel('Время, с')\n",
    "ax2.set_xlabel('Повторения')\n",
    "ax2.set_xticks(iterations)\n",
    "# ax2.set_xticklabels(range(1, 6))\n",
    "\n",
    "ax2.scatter(iterations, ll_sorted_delete, label='связный список', color=ll_col)\n",
    "ax2.axhline(y=ll_sorted_delete_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n",
    "\n",
    "ax2.scatter(iterations, ht_sorted_delete, label='хеш таблица', color=ht_col)\n",
    "ax2.axhline(y=ht_sorted_delete_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n",
    "\n",
    "ax2.scatter(iterations, bst_sorted_delete, label='дерево', color=bst_col)\n",
    "ax2.axhline(y=bst_sorted_delete_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n",
    "\n",
    "ax2.legend()\n",
    "ax2.grid(True, alpha=0.3)\n",
    "\n",
    "# Общий заголовок\n",
    "plt.suptitle(f'Сравнение времени удаления в структурах данных (N = {countDeletes})', fontsize=14)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('../img/delete.pdf', \n",
    "            format='pdf',\n",
    "            dpi=300,\n",
    "            bbox_inches='tight', \n",
    "            pad_inches=0.1)\n",
    "plt.show()"
   ]
  },
  {
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